The segmentation model is trained with a cross-entropy loss function, the auto-encoder with the mean squared error, and the registration network with the loss given by Eq. REF . The optimization algorithm for all models is ADAM [1]}, the initial learning rate is \(10^{-4}\) , decreasing by a factor of 10 each time the validation loss plateaus. All models are trained until convergence. Due to the large 3D volumes involved, the choice of batch-size is often limited by available memory. We sum gradients over multiple passes to arrive at effective batch-sizes of 3-5 samples. The hyperparameter \(\lambda \)  is tuned for each model independently on the validation set, we plot the validation score of tested values in Figure REF .
